The idea of increasing returns has come up every few decades but Brian Arthur’s precise and fully-modeled papers caused us to clearly understand what kinds of models have what kinds of implications. One outstanding characteristic of Arthur’s viewpoint is emphatically dynamic in nature. Learning by using or doing plays an essential role, as opposed to static examples of returns to scale (those based on volume-area relations). The object of study is a history. Another distinctive feature of most of the work is its stochastic character. This permits emphasis on the importance of random deviations for long-run tendencies. Other tendencies include the multiplicity of possible long-run states, depending on initial conditions and on random fluctuations over time, and the specialization (in terms of process or geographical location) in an outcome achieved. Increasing returns may also serve as a reinforcement for early leading positions and so act in a manner parallel to more standard forms of increasing returns. A similar phenomenon occurs even in individual learning, where again successes reinforce some courses of action and inhibit others, thereby causing the first to be used more intensively, and so forth. There are in all of these models opposing tendencies, some toward achieving an optimum, some toward locking in on inefficient forms of behavior.

Key Takeaways

The papers here reflect two convictions I have held since I started work in this area. The first is that increasing returns problems tend to show common properties and raise similar difficulties and issues wherever they occur in economics. The second is that the key obstacle to an increasing returns economics has been the “selection problem” – determining how an equilibrium comes to be selected over time when there are multiple equilibria to choose from. Thus the papers here explore these common properties – common themes – of increasing returns in depth. And several of them develop methods, mostly probabilistic, to solve the crucial problem of equilibrium selection.

Arthur studied electrical engineering so was vaguely familiar with positive feedback already and became more intrigued when he read about the history of the discovery of the structure of DNA and read whatever he could about molecular biology and enzyme reactions and followed these threads back to the domain of physics. In this work, outcomes were not predictable, problems might have more than one solution, and chance events might determine the future rather than be average away. The key to this work, I realized, lay not in the domain of the science it was dealing with, whether laser theory, or thermodynamics, or enzyme kinetics. It lay in the fact that these were processes driven by some form of self-reinforcement, or positive feedback, or cumulative causation – processes, in economics terms that were driven by nonconvexities. Here was a framework that could handle increasing returns.

Great discoveries tend to come from outside the field

Polya Process – path-dependent process in probability theory

In looking back on the difficulties in publishing these papers, I realize that I was naive in expecting that they would be welcomed immediately in the journals. The field of economics is notoriously slow to open itself to ideas that are different. The problem, I believe is not that journal editors are hostile to new ideas. The lack of openness stems instead from a belief embedded deep within our profession that economics consists of rigorous deductions based on a fixed set of foundational assumptions about human behavior and economic institutions. If the assumptions that mirror reality are indeed etched in marble somewhere, and apply uniformly to all economics problems, and we know what they are, there is of course no need to explore the consequences of others. But this is not the case. The assumptions economists need to use vary with the context of the problem and cannot be reduced to a standard set. Yet, at any time in the profession, a standard set seems to dominate. I am sure this state of affairs is unhealthy. It deters many economists, especially younger ones, from attempting approaches or problems that are different. It encourages use of the standard assumptions in applications where they are not appropriate. And it leaves us open to the charge that economics is rigorous deduction based upon faulty assumptions. At this stage of its development economics does not need orthodoxy and narrowness; it needs openness and courage.

I did not set out with an intended direction but if I have had a constant purpose it is to show that transformation, change, and messiness are natural in the economy. The increasing-returns world in economics is a world where dynamics, not statics, are natural; a world of evolution rather than equilibrium; a world or probability and chance events. Above all, it is a world of process and pattern change

Positive Feedbacks in the Economy

Diminishing returns, what conventional economic theory is built around, imply a single economic equilibrium point for the economy, but positive feedback – increasing returns – makes for many possible equilibrium points. There is no guarantee that the particular economic outcome selected from among the many alternatives will be the “best” one. Furthermore, once random economic events select a particular path, the choice may become locked-in regardless of the advantages of the alternatives

Increasing returns do not apply across the board – agriculture and mining (resource-based portions) – are subject to diminishing returns caused by limited amounts of fertile land or high quality deposits. However, areas of the economy which are knowledge-based are largely subject to increasing returns. Even the production of aircraft is subject to increasing returns – it takes a large initial investment but each plane after that is only a fraction of the initial cost. In addition, producing more units means gaining more experience in the manufacturing process and achieving greater understanding of how to produce additional units even more cheaply. Moreover, experience gained with one product or technology can make it easier to produce new products incorporating similar or related technologies. Not only do the costs of producing high-technology products fall as a company makes more of them, but the benefits of using them increase. Many items such as computers or telecommunications equipment work in networks that require compatibility; when one brand gains a significant market share, people have a strong incentive to buy more of the same product so as to be able to exchange information with those using it already.

Timing is important too in the sense that getting into an industry that is close to being locked in makes little sense. However, early superiority does not correlate with long term fitness

Like punctuated equilibrium, most of the time the perturbations are averaged away but once in a while they become all important in tilting parts of the economy into new structures and patterns that are then preserved and built on in a fresh layer of development

There is an indeterminacy of outcome, nonergodicity (path dependence where small events cumulate to cause the systems to gravitate towards that outcome rather than others). There may be potential inefficiency and nonpredictability. Although individual choices are rational, there is no guarantee that the side selected is, from any long term viewpoint, the better of the two. The dynamics thus take on an evolutionary flavor with a “founder effect” mechanism akin to that in genetics

Path dependent processes and the emergence of macrostructure

Many situations dominated by increasing returns are most usefully modeled as dynamic processes with random events and natural positive feedbacks or nonlinearities. We call these nonlinear Polya processes and show that they can model a wide variety of increasing returns and positive feedback problems. In the presence of increasing returns or self reinforcement, a nonlinear Polya process typically displays a multiplicity if possible asymptotic outcomes. Early random fluctuations cumulate and are magnified or attenuated by the inherent nonlinearities of the process. By studying how these build up as the dynamics of the process unfold over time, we can observe how an asymptotic outcomes becomes “selected” over time

Very often individual technologies show increasing returns to adoption – the more they are adopted the more is learned about them; in then the more they are improved, and the more attractive they become. Very often, too, there are several technologies that compete for shares of a “market” of potential adopters

Industry location patterns and the importance of history

This study indeed shows that it is possible to put a theoretical basis under the historical-accident-plus-agglomeration argument (mostly arbitrary location for determining where a city is established but then more people flock to it, it receives more investment, more buildings come up, etc. which leads to agglomeration and increasing returns).

Information Contagion

When a prospective buyer is making purchasing decisions among several available technically-based products, choosing among different computer workstations, say, they often augment whatever publicly available information they can find by asking previous purchasers about their experiences – which product they chose, and how it is working for them. This is a natural and reasonable procedure; it adds information that is hard to come by otherwise. But it also introduces an “information feedback” into the process whereby products compete for market share. The products new purchasers learn about depend on which products the previous purchasers “polled” or sampled and decided to buy. They are therefore likely to learn more about a commonly purchased product than one with few previous users. Hence, where buyers are risk-averse and tend to favor products they know more about, products that by chance win market share early on gain an information-feedback advantage. Under certain circumstances a product may come to dominate by this advantage alone. This is the information contagion phenomenon

Self-Reinforcing Mechanisms in Economics

Dynamical systems of the self-reinforcing or autocatalytic type – systems with local positive feedbacks – in physics, chemical kinetics, and theoretical biology tend to possess a multiplicity of asymptotic states or possible “emergent structures”. The initial starting state combined with early random events or fluctuations acts to push the dynamics into the domain of one of these asymptotic states and thus to “select” the structure that the system eventually “locks into”.

Self-reinforcing mechanisms are variants of or derive from four generic sources:

Large set up or fixed costs (which give the advantage of falling unit costs to increased output)

Learning effects (which act to improve products or lower their cost as their prevalence increases)

Self-reinforcing expectations (where increased prevalence on the market enhances beliefs of further prevalence)

Besides these 4 properties, we might note other analogies with physical and biological systems. The market starts out even symmetric, yet it ends up asymmetric: there is “symmetry breaking.” An “order” or pattern in market shares “emerges” through initial market “fluctuations.” The two technologies compete to occupy one “niche” and the one that gets ahead exercises “competitive exclusion” on its rival. And if one technology is inherently superior and appeals to a larger proportion of purchasers, it is more likely to persist: it possesses “selectional advantage.”

Some more characteristics: multiple equilibria (multiple “solutions” are possible but the outcome is indeterminate, not unique and predictable); possible inefficiency, lock-in, path dependence

We can say that the particular equilibrium is locked in to a degree measurable by the minimum cost to effect changeover to an alternative equilibrium. In many economic systems, lock-in happens dynamically, as sequential decisions “groove” out an advantage that the system finds it hard to escape from. Exiting lock-in is difficult and depends on the degree to which the advantages accrued by the inferior “equilibrium” are reversible or transferable to an alternative one. It is difficult when learning effects and specialized fixed costs are the source of reinforcement. Where coordination effects are the source of lock-in, often advantages are transferable. As long as each user has certainty that the others also prefer the alternative, each will decide independently to “switch”. Inertia must be overcome though because few individuals dare change in case others do not follow

Path Dependence, Self-Reinforcement, and Human Learning

There is a strong connection between increasing returns mechanisms and learning problems. Learning can be viewed as competition among beliefs or actions, with some reinforced and others weakened as fresh evidence and data are obtained. But as such, the learning process may then lock-in to actions that are not necessarily optimal nor predictable, by the influence of small events

What makes this iterated-choice problem interesting is the tension between exploitation of knowledge gained and exploration of poorly understood actions. At the beginning many actions will be explored or tried out in an attempt to gain information on their consequences. But in the desire to gain payoff, the agent will begin to emphasize or exploit the “better” ones as they come to the fore. This reinforcement of “good” actions is both natural and economically realistic in this iterated-choice context; and any reasonable algorithm will be forced to take account of it.

Strategic Pricing in Markets and Increasing Returns

Overall, we find that producers’ discount rates are crucial in determining whether the market structure is stable or unstable. High discount rates damp the effect of self-reinforcement and lead to a balanced market, while low discount rates enhance it and destabilize the market. Under high discount rates, firms that achieve a large market share quickly lose it again by pricing high to exploit their position for near-term profit. And so, in this case the market stabilizes. Under low discount rates, firms price aggressively as they struggle to lock in a future dominant position; and when the market is close to balanced shares, each drops its price heavily in the hope of reaping future monopoly rents. The result is a strong effort by each firm to “tilt” the market in its favor, and to hold it in an asymmetric position if successful. And so, in this case strategic pricing destabilizes the market

The simple dynamics and stochastic model of market competition analyzed in this paper reveals striking properties. First, positive feedback or self-reinforcement to market share may result in bistable stationary distributions with higher probabilities assigned to asymmetric market shares. The stronger the positive feedback, the lower the probability of passing from the region of relative prevalence of one product to that of the other. Second, when producers can influence purchase probabilities by prices, in the presence of positive feedback, optimal pricing is highly state-dependent. The producers struggle for market shares by lowering prices, especially near pivot states with balanced shares.

The fundamental problem does not lie in any particular System but rather in Systems as Such. Salvation, if it is attainable at all, even partially, is to be sought in a deeper understanding of the ways of all Systems, not simply in a criticism of the errors of a particular system. Systems are seductive. They promise to do a hard job faster, better, and more easily than you could do it by yourself. But if you setup a System, you are likely to find your time and effort now being consumed in the care and feeding of the system itself. New problems are created by its very presence. Once set up, it won’t go away; it grows and encroaches. It begins to do strange and wonderful things and breaks down in ways you never thought possible. It kicks back, gets in the way and opposes its own proper function. Your own perspective becomes distorted by being in the system. You become anxious and push on it to make it work. Eventually you come to believe that the misbegotten product it so grudgingly delivers is what you really wanted all the time. At that time, encroachment is complete. You have become absorbed. You are now a systems-person.

Key Takeaways

Systemism – mindless belief in systems, that they can be made to function to achieve desired goals. The strange behavior (antics) of complex systems

Systems Never Do What We Really Want Them to Do

Malfunction is the rule and flawless operation the exception. Cherish your system failures in order to best improve

The height and depth of practical wisdom lies in the ability to recognize and not to fight against the Laws of Systems. The most effective approach to coping is to learn the basic laws of systems behavior. Problems are not the problem; coping is the problem

Systems don’t enjoy being fiddled with and will react to protect themselves and the unwary intervenor may well experience an unexpected shock

Failure to function as expected is to be expected. It is a perfectly general feature of systems not to do what we expected them to do.

“Anergy” is the unit of human effort required to bring the universe into line with human desires, needs, or pleasures. The total amount of anergy in the universe is constant. While new systems may reduce the problem it set out to, it also produces new problems.

Once a system is in place, it not only persists but grows and encroaches

Reality is more complex than it seems and complex systems always exhibit unexpected behavior. A system is not a machine. It’s behavior cannot be predicted even if you know it’s mechanism

Systems tend to oppose their own proper functions. There is always positive and negative feedback and oscillations in between. The pendulum swings

Systems tend to malfunction conspicuously just after their greatest triumph. The ghost of the old system continues to haunt the new

People in systems do not do what the system says they are doing. In the same vein, a larger system does not do the same function as performed as the smaller system. The larger the system the less the variety in the product. The name is most emphatically not the thing

To those within a system, the outside reality tends to pale and disappear. They are experiencing sensory deprivation (lack of contrasting experiences) and experience an altered mental state. A selective process occurs where the system attracts and keeps those people whose attributes are such as are attracted them to life in that system: systems abstract systems people

The bigger the system, the narrower and more specialized the interface with individuals (SS number rather than dealing with a human)

Systems delusions are the delusion systems that are almost universal in our modern world

Designers of systems tend to design ways for themselves to bypass the system. If a system can be exploited, it will and any system can be exploited

If a big system doesn’t work, it won’t work. Pushing systems doesn’t help and adding manpower to a late project typically doesn’t help. However, some complex systems do work and these should be left alone. Don’t change anything. A complex system that works is invariably found to have evolved from a simple system that worked. A complex system designed from scratch never works and can not be made to work. You have to start over, beginning with a working simple system. Few areas offer greater potential reward than understanding the transition from working simple system to working complex system

In complex systems, malfunction and even total non function may not be detectable for long periods, if ever. Large complex systems tend to be beyond human capacity to evaluate. But whatever the system has done before, you can be sure it will do again

The system is its own best explanation – it is a law unto itself. They develop internal goals the instant they come into being and these goals come first. Systems don’t work for you or me. They work for their own goals and behaves as if it has a will to live

Most large systems are operating in failure mode most of the time. So, it is important to understand how it fails, how it works when it’s components aren’t working well, how well does it work in failure mode. The failure modes can typically not be determined ahead of time and the crucial variables tend to be discovered by accident

There will always be bugs and we can never be sure if they’re local or not. Cherish these bugs, study them for they significantly advance you towards the path of avoiding failure. Life isn’t a matter of just correcting occasional errors, bugs, or glitches. Error-correction is what we are doing every instant of our lives

Form may follow function but don’t count on it. As systems grow in size and complexity, they tend to lose basic functions (supertankers can’t dock)

Colossal systems cause colossal errors and these errors tend to escape notice. If it is grandiose enough, it may not even be comprehended as an error (50,000 Americans die each year in car accidents but it is not seen as a flaw of the transportation system, merely a fact of life.) Total Systems tend to runaway and go out of control

In setting up a new system, tread softly. You may be disturbing another system that is actually working

It is impossible not to communicate – but it isn’t always what you want. The meaning of a communication is the behavior that results

Knowledge is useful in the service of an appropriate model of the universe, and not otherwise. Information decays and the most urgently needed information decays fastest. However, one system’s garbage is another system’s precious raw material. The information you have is not the information you want. The information you want is not the information you need. The information you need is not the information you can obtain.

In a closed system, information tends to decrease and hallucination tends to increase

What Can Be Done

Inevitability-of-Reality Fallacy – things have to be the way they are and not otherwise because that’s just the way they are. The person or system who has a problem and doesn’t realize it has two problems, the problem itself and the meta-problem of unawareness

Problem avoidance is the strategy of avoiding head-on encounters with a stubborn problem that does not offer a good point d’appui, or toe hold. It is the most under-rated of all methods of dealing with problems. Little wonder, for its practitioners are not to be found struggling valiantly against staggering odds, nor are they to be seen fighting bloody but unbowed, nor are they observed undergoing glorious martyrdom. They are simply somewhere else, successfully doing something else. Like Lao Tzu himself, they have slipped quietly away into a happy life of satisfying obscurity. The opposite of passivity is initiative, or responsibility – not energetic futility. Choose your systems with care. Destiny is largely a set of unquestioned assumptions

Creative Tack – if something isn’t working, don’t keep doing it. Do something else instead – do almost anything else. Search for problems that can be neatly and elegantly solved with the resources (or systems) at hand. The formula for success is not commitment to the system but commitment to Systemantics

The very first principle of systems-design is a negative one: do without a new system if you can. Two corollaries: do it with an existing system if you can; do it with a small system if you can.

Almost anything is easier to get into than out of. Taking it down is often more tedious than setting it up

Systems run best when designed to run downhill. In essence, avoid uphill configurations, go with the flow. In human terms, this means working with human tendencies rather than against them. Loose systems last longer and function better. If the system is built too tight it will seize up, peter out, or fly apart. Looseness looks like simplicity of structure, looseness in everyday functioning; “inefficiency” in the efficiency-expert’s sense of the term; and a strong alignment with basic primate motivations

Slack in the system, redundancy, “inefficiency” doesn’t cost, it pays

Bad design can rarely be overcome by more design, whether bad or good. In other words, plan to scrap the first system when it doesn’t work, you will anyway

Calling it “feedback” doesn’t mean that it has actually fed back. It hasn’t fed back until the system changes course. The reality that is presented to the system must also make sense if the system is to make an appropriate response. The sensory input must be organized into a model of the universe that by its very shape suggests the appropriate response. Too much feedback can overwhelm the response channels, leading to paralysis and inaction. The point of decision will be delayed indefinitely, and no action will be taken. Togetherness is great, but don’t knock get-away-ness. Systems which don’t know how much feedback there will be or which sources of feedback are critical, will begin to fear feedback and regard it as hostile, and even dangerous to the system. The system which ignores feedback has already begun the process of terminal instability. This system will be shaken to pieces by repeated violent contact with the environment it is trying to ignore. To try to force the environment to adjust to the system, rather than vice versa, is truly to get the cart before the horse

What the pupil must learn, if he learns anything, is that the world will do most of the work for you, provided you cooperate with it by identifying how it really works and identifying with those realities. – Joseph Tussman

Nature is only wise when feedbacks are rapid. Like nature, systems cannot be wise when feedbacks are unduly delayed. Feedback is likely to cause trouble if it is either too prompt or too slow. However, feedback is always a picture of the past. The future is no more predictable now than it was in the past, but you can at least take note of trends. The future is partly determined by what we do now and it’s at this point that genuine leadership becomes relevant. The leader sees what his system can become. He has that image in mind. It’s not just a matter of data, it’s a matter of the dream. A leader is one who understands that our systems are only bounded by what we can dream. Not just ourselves, but our systems also, are such stuff as dreams are made on. It behooves us to look to the quality of our dreams

Catalytic managership is based on the premise that trying to make something happen is too ambitious and usually fails, resulting in a great deal of wasted effort and lowered morale. On the other hand, it is sometimes possible to remove obstacles in the way of something happening. A great deal may then occur with little effort on the part of the manager, who nevertheless (and rightly) gets a large part of the credit. Catalytic managership will only work if the system is so designed that something can actually happen – a condition that commonly is not met. Catalytic managership has been practiced by leaders of genius throughout recorded history. Gandhi is reported to have said, “There go my people. I must find out where they are going, so I can lead them.” Choosing the correct system is crucial for success in catalytic managership. Our task, correctly understood, is to find out which tasks our system performs well and use it for those. Utilize the principle of utilization

The system itself does not solve problems. The system represents someone’s solution to a problem. The problem is a problem precisely because it is incorrectly conceptualized in the first place, and a large system for studying and attacking the problem merely locks in the erroneous conceptualization into the minds of everyone concerned. What is required is not a large system, but a different approach. Solutions usually come from people who see in the problem only an interesting puzzle, and whose qualifications would never satisfy a select committee. Great advances do not come out of systems designed to produce great advances. Major advances take place by fits and starts

Most innovations and advancements come from outside the field

It is generally easier to aim at changing one or a few things at a time and then work out the unexpected effects, than to go to the opposite extreme, attempting to correct everything in one grand design is appropriately designated as grandiosity. In dealing with large systems, the striving for perfection is a serious imperfection. Striving for perfection produces a kind of tunnel-vision resembling a hypnotic state. Absorbed in the pursuit of perfecting the system at hand, the striver has no energy or attention left over for considering others, possibly better, ways of doing the whole thing

Nipping disasters in the bud, limiting their effects, or, better yet, preventing them, is the mark of a truly competent manager. Imagination in disaster is required – the ability to visualize the many routes of potential failure and to plug them in advance, without being paralyzed by the multiple scenarios of disaster thus conjured up. In order to succeed, it is necessary to know how to avoid the most likely ways to fail. Success requires avoiding many separate possible causes of failure.

In order to be effective, an intervention must introduce a change at the correct logical level. If your problem seems unsolvable, consider that you may have a meta problem

Control is exercised by the element with the greatest variety of behavioral responses – always act so as to increase your options. However, we can never know all the potential behaviors of the system

The observer effect – the system is altered by the probe used to test it. However, there can be no system without its observer and no observation without its effects

Look for the self-referential point – that’s where the problem is likely to be (nuclear armament leading to mutually assured destruction)

Be weary of the positive feedback trap. If things seem to be getting worse even faster than usual, consider that the remedy may be at fault. Escalating the wrong solution does not improve the outcome. The author proposes a new word, “Escalusion” or “delusion-squared orD2“, to represent escalated delusion

If things are acting very strangely, consider that you may be in a feedback situation. Alternatively, when problems don’t yield to commonsense solutions, look for the “thermostat” (the trigger creating the feedback)

The remedy must strike deeply at the roots of the system itself to produce any significant effect

Reframing is an intellectual tool which offers hope of providing some degree of active mastery in systems. A successful reframing of the problem has the power to invalidate such intractable labels as “crime”, “criminal”, or “oppressor” and render them as obsolete and irrelevant as “ether” in modern physics. When reframing is complete, the problem is not “solved” – it doesn’t even exist anymore. There is no longer any problem to discuss, let alone a solution. If you can’t change the system, change the frame – it comes to the same thing. The proposed reframing must be genuinely beneficial to all parties or it will produce a destructive kickback. A purported reframing which is in reality an attempt to exploit will inevitably be recognized as such sooner or later. The system will go into dense mode and all future attempts to communicate will be viewed as attempts to exploit, even when not so motivated

Everything correlates – any given element of one system is simultaneously an element in an infinity of other systems. The fact of linkage provides a unique, subtle, and powerful approach to solving otherwise intractable problems. As a component of System a, element x is perhaps inaccessible. But as a component of System B, C, or D…it can perhaps be affected in the desired direction by intervening in System B, C, D…

In order to remain unchanged, the system must change. Specifically, the changes that must occur are changes in the patterns of changing (or strategies) previously employed to prevent drastic internal change. The capacity to change in such a way as to remain stable when the ground rules change is a higher-order level of stability, which fully deserves its designation as Ultra-stability

What I got out of it

A fun and sarcastic read about systems, their general behavior, how difficult they are to change, and much more.

This books dives into the power of networks and how the interconnectedness of everything changes everything. The seventh sense lies in harnessing and understanding the power of networks. The seventh sense is the ability to look at any object and see how it is changed by connection. Connection changes the nature of an object

Key Takeaways

The sixth sense was Nietzsche’s idea of people having to get a sense for the rhythm of history in order to deal with the then nascent industrial revolution. The seventh sense allows us to change our habits in order to deal with our new networked world where we are constantly connected. Any system which is not designed around or to work with constant connection will have to be re-thought and re-designed or else they will crack in today’s new paradigm

In a world where the map is constantly changing due to the power networks have to effect change, one has to rely on their instincts or seventh sense in order to survive and thrive. Mastery of the seventh sense will give people an ability to instantly feel and react to the changing power of networks

Understanding always takes a long time, contemplation, stillness and deep conversations with others in order to penetrate and embody the truth.

Networks grow as they gain nodes which connect across areas, mediums and geography. Networks grow powerful as they expand and depends on the type and relationship and speed of the nodes. Helpful metaphor for world of punctuated equilibrium is when molecules of water molecules link up and suddenly turns to ice when the temperature drops low enough. A powerful network can develop and disrupt that quickly

Protocols allow you to design the organization and processes of a system and therefore the design of the protocol gives you almost total control of the system. Awesome example where he says that learning Chinese fluently is not what will be important in the future but creating the translation protocol will be because this controls the system and makes one specific language less important

The physical world is shaped and influenced by the design of the digital world

America is different than previous superpowers in the sense that she is willing to change and disrupt what makes her so dominant today

So much of what is brilliant and revolutionary at first is seen as crazy, comical and/or stupid

Greatest threat to America dominance is not China, Russia, or anyone else but the evolution of networks and the power they bring

The victors of the future will be those who master networks

What marks successful network thinkers is that they see structures with in the network and how power and influence might move through them

That work create centralization and distribution. Network notes are distributed all over the world but power accrued and centralizes along the most powerful company. Distribution and concentration are the essence of power now. As more devices and those connect to the network the more powerful the core have to become

The “platform used to matter but now it is Proto call indicate how powerful pipeline in the design a system have become

Leaders are struggling today because of the tension between centralization and distribution. This may be in paradoxical but much light in and day they coexist and need to other in order to exist exist

System and networks are complex adaptive systems where unexpected behaviors and pattern emerge.

Once an object connect to a system turns from complicated to complex. Industrial revolution made a simple complicated and network revolution made the complicated complex

Robust principal – conservative in what you do and liberal in what you accept

Conductivity brings with it multiple downside such as owner ability to have to wear your machine can be taken over and commanded what to do. All of the worlds most relied upon system and from the political to financials are vulnerable to sources that are hard to stop and even harder to see. Most of the biggest decision that will affect our lives will from now on be done and implemented in secret

New cast of it being for who truly understands how to make computers that work at systems say and how they should be designed. Betty ourselves cause but I’ll have the MC of the seven cents

Seventh son does not want a level of acidity towards these new network but rather an understanding of its nature in order to further what we really care about

At work are fundamentally interwoven with time and often try heated up or manipulated in other ways

How we perceive is greatly influenced by in which we experience for example climbing a hill on foot versus in a car are almost totally different experience

In the end that works for Bill to get around time by there nearly instantaneous function. As it up space becomes compress as you can travel the same distance and last time. Bass is a competitive advantage to be faster is the sizes for the future lies those who are the fastest

Old world in terms of distance where as the new cast thing in terms of how fast you can traverses. Geography versus whole policy. Today how far away something is truly determined by the speed and quality of the connection and not by distance

The true power of network lies in trust ties and not in cables or fiber

Influence will accrue to those who are better able to compress time as there seems to be on unlimited desire for speed

Today there is no more powerful position that to a network which is desirable for others. Figuring out where our who is our will help you figure out what steps to take and whether to build your own pasta or do something new

Networks allow for a relatively new phenomenon which turns diminishing returns into increasing returns. Network effects are the strongest type of increasing return and changes how we think about and operate businesses. These businesses are power law distributed as they breed commanding winners – winner take all. Networks optimize themselves to be faster and more efficient the bigger they get and the more people who use them. Winner takes all because we all benefit

A network and connected world wants the fastest solution to everything from social media to videos to online payment

When the Chinese want to do something they asked what is the nature of the age as the context matters as much as the solution but I Americans simply begin with a goal. The author posit that today’s nature is one of collapse of old industry and giant and construction of new one

That works give us a glimpse into where power is and will be in the strategies one should employ based on this

The fundamental question of power in today’s age is whether you or your country or company are the gatekeeper or gatekept. The nature of today is that everything is or will be connected and as we’ve learned, this changes the nature of the object connected. This process of linking everything is unstoppable

An increasingly powerful tool will be the ability to cut and keep people out of these important gatelands

Disappearing AI if the concept that AI is getting so powerful. So much data entry so many connections that how it got to a dancer is becoming increasingly impossible for humans to figure out this has in Norman and location for our future and is exciting and scary

Summary

Fascinating book on the power of networks and how the ability to see how the nature of things change once they are connected is where future power, innovation, influence and wealth lies.

There are some simple, universal laws which link all complex systems and this book explores these various scaling laws and systems to provide a predictive framework

Key Takeaways

We live in an exponentially expanding socio-economic urbanized worked. A key topic of this book is the key role that cities and urbanization play in the future of the world. Cities are the crucible of civilization and humans depend on their continuity for innovation, commerce and as magnets for creativity and growth. However, they also attract crime, resources and cause much pollution and health problems. The author will explore if there can be a science of cities and companies to predict their longevity and provide a framework and strategy for achieving long term sustainability. Mega cities will take on scales never before seen as the trend is for increasing urbanization with up to 75% of the world population living in cities by 2050

The open ended exponential growth of cities is in marked contrast to what is seen in biology and in corporations. To what extent are cities extensions of biology and if they are in fact a type of super organism why do most of them never die? The author will explore whether a serious mechanistic theory can explain our own mortality and that of companies and why cities seem immune

Too short of a time frame to tell? 5-10,000 is a lot but on geological scales it is not at all

Energy, metabolism and entropy. Will refer to all types of biological energy transformation as metabolism which are used towards physical work as well as for refueling, growth, reproduction and maintenance. The vast majority of human energy has been put towards forming collectives such as cities and companies as well as to the discovery and implementation of ideas. However, without a steady supply of energy none of this growth or innovation is possible. Energy is primary to everything we do and everything that happens around us and is often underapprecaited by scientists and researchers in most fields. As there is no free lunch regarding energy, every action and energy usage has a consequence in the form of useless energy in the form of heat or disorder, otherwise known as entropy. Entropy, the second law of thermodynamics, always increases and hangs over everyone and everything. Growth stops because of the mismatch between energy demand and supply. However, things which grow super linearly, like cities, get more energy supplied than is demanded as they grow so as they get bigger they also grow faster. For companies, sales or revenues can be thought of as growth and expenses and maintenance. The fewer expenses the more energy is available for growth. Younger company’s profits scale faster than expenses but on average these scale linearly as companies grow (similar to the progress of most organisms). Companies tend to grow, scale, mature and die in similar fashions regardless of industry which indicates universal dynamics may be at play

Scaling and non linear behavior. Scaling is how things change with size and the fundamental rules and behaviors they obey. This helps establish a framework to connect how various systems, organisms and more behave as they change size. Scaling helps understand tipping points, chaotic systems and phase transitions. Scaling will play an increasingly larger role as man made systems continue to increase in size and complexity and underlying principles are typically not well understood as they tend to becomplex adaptive systems. Linear extrapolation to growth and scaling is dangerous as it is often implicit as it is often wrong. Metabolic needs, parents, innovation and much more scales non-linearly or enjoys increasing returns to scale – LA’s GDP per capita is greater than expected when compared to Oklahoma City’s GDP per capita. Economies of scale – as a city, organism, etc gets larger it in fact gets more efficient. An organism twice as large only requires 75% more energy rather than 100% as linear thinking would suggest. This 3/4 metabolic scaling law applies across nearly every taxonomy. The number 4 therefore plays a nearly universal law in biological life. Elephants, though having 10,000 more cells to support than rats, only need 1,000 more energy. This amazing efficiency allows for longer longevity

Emergence, self organization and resilience. Complex systems are made up of a multitude of small components which lead to unexpected results where the whole is greater than the sum of its parts, emergence. Humans are more than the totality of their cells. There is no central control by these individual components but amazing things can emerge through this self-organization. These complex adaptive systems are able to adapt and evolve to changing external conditions leading to a resilient system. However, these systems are also influenced by both positive and negative feedback loops which can quickly alter the system

You are your networks. Growth can be considered a special case of scaling. Networks the determine the rates at which energy and resources are delivered to cells, they set the pace of all physiological processes. Pace of life slows as the network expands which is why bigger animals live longer, take longer to mature, have slower heart rates and are more metabolically efficient

Cities tend to scale at a 1.15 scaling law as it doubles. So, a city twice as bi has 15% more innovation, wages, crime, disease, etc. than a linear doubling would suggest. This appears across countries and across time showing there may be a universal, generic scaling law we can apply to cities. Pace of life in bugger cities scaled as well and people’s pace of walking even does too. This scaling also indicates that pace of innovation and wealth creation must keep speeding up as well. This must break at some point however so growth may hold the seeds of its own destruction – dialectical materialism

Companies are much more like organisms than cities in that they scale sub linearly (0.85), they get more efficient and slower as they get bigger, rather than faster like cities (1.15 scaling). Companies, like organisms, also stop growing at some point, slow down and eventually decay as changing and growing becomes more difficult the larger one is, they become more unidimensional whereas cities get multidimensional as they grow

Understanding that area and volume scale at different proportions is helpful when thinking about scaling up houses, organisms, etc. If the size of an elephant doubles, the weight of the elephant grows in proportion to the volume (which cubes if the animal is doubled) whereas the strength would only double. That is why ants the size of elephants or Godzilla could exist if they were made of the same materials. There are limits to size and growth as the relative strength decreases as size increases. There is a nonlinear growth scale between strength and weight (2:3)

A key assumption of scaling is that the physical and chemical compositions remain the same. However, innovation often allows for growth at a larger scale due to stronger materials, like steel instead of wood, or improved design such as the use of arches or vaults

Brunell – one of the greatest engineers of the 19th century and a true polymath. He innovated with tunnels, railways, shipbuilding (larger ships require proportionately less fuel per ton than smaller ships, economies of scale), bridges and more

The Navier-Stokes equation describes turbulence and was very influential in shipbuilding and was one of the first studies of complexity

Metabolic rate is the fundamental rate for all of biology, setting the pace of everything life does. Metabolism may be the most pervasive and consistent law in the universe, applying to some orders of magnitude of mass, from bacteria to blue whales

The Magic #4 – a huge range of scaling laws across life (metabolism, heart rate, size of aorta, tree trunk) scale in simple quarters suggesting that evolution has been constrained by other general principles beyond natural selection. This may be a clue to universal biological principles which could help us better predict and analyze life. Networks may be the constraint which leads to quarter scaling as the physical makeup of the network may be different but they would be constrained by the same mathematical and physical principles. Power law scaling is the mathematical expression of self similarity and fractality. We are thus all living examples of self similarity and fractals. My understanding is that this quarter scaling is indicative of our 4D universe (the fourth being fractality or self-similarity which takes advantage of volume filling traits) and contrary to the 11D string theorists currently believe we live in. Few human inventions take advantage of the optimization fractals confer but organic processes like organisms and cities do.

Humans require approximately 90w of energy to live

What is irrelevant at one scale becomes dominant at another. What is important at every scale is to find the variables which dominate the behavior of the system. See Game Play video with Alan Watts narration which Kevin Rose pointed out which discusses perspective and scale

Optimization principles lie at the very heart of all of the fundamental laws of nature as all aim to minimize the amount of action or energy required. Thus, though the networks are physically different, animals and plants scale similarly to minimize the amount of energy needed for energy to reach the terminal destination (capillaries). Can say the blood changes from AC (pulsatile) to DC (steady stream) as it moves to the capillaries. This saves energy and ensures the blood is moving slow enough for oxygen to dissipate

Impedence matching is when there is clear and accurate communication, saving energy

Although organisms take advantage of optimization from self similarity, the physical bounds of the networks limits the size, age, scope, etc of physical organisms. Weight would crush the animal as volume scales faster than area, oxygen would not be able to diffuse into cells once animal reaches a certain size. Organisms also stop growing due to the different ways energy need and metabolism scale. The rate at which energy is needed for maintenance scales faster than the rate at which metabolic energy can be supplied, forcing the amount of energy for growth to systematically decrease, resulting in the cessation of growth at some point. So, the less energy needed for maintenance (fixed costs), the more is available for growth.

Life is so sensitive to temperature because chemical reactions such as metabolic rate scale exponentially in respect to temperature. For example, a 2°C rise in temperature would lead to a 20-30% rise in pace of life (and hence mortality)

The author believes that caloric restriction can increase lifespan as anything which slows down the metabolism will lead to less damaged cells in a similar period of time, thereby increasing life span. However, we are complex adaptive systems and simply altering one variable will have unseen consequences

Amazingly, the universe is expanding exponentially and on earth, we are expanding exponentially socioeconomically

Traditionally, population growth has correlated closely with increases in financial indices

The city is our ingenious invention to increase collaboration and social cohesion and interaction. Two key components of innovation and wealth creation. Their downside include crime, pollution and huge consumption or resources. Cities are an emergent self-organizing phenomenon helping increase productivity, social collaboration and innovation no matter where in the world the city is

Dunbar’s Number is a nested group in that your most intimate friends number around 5, second tier is about 15, then 50 and 150 and so on in multiples of three. This can be used to form optimally sized groups, councils, etc. The author speculates that cities are physical manifestations of our brain as their function and basic layout are universal and symbolic of how humans act and interact which is encoded in our neural networks

Zipf’s Law is used to describe the size and frequency distribution of a huge array of areas. It says that the second largest or more st frequent will be about half as large or frequent as the first, the third about 1/3, fourth about 1/4, etc. Another way of stating Pareto’s 80/20 Law

People, regardless of city or occupation, spend about 1 hour per day commuting. All technology has done has allowed people to live farther away as they can now travel faster

Social interactions underlie the universal scaling of urban characteristics

Cities which are rich, safe, innovative and generally overperforming, tend to keep doing so and similar for cities which underperform

Different types of businesses and professions also scale proportionately as cities grow, some super and some sub-linearly

To sustain open ended growth in light of resource limitation requires continuous cycles of paradigm shifting innovations which over time must occur at shorter and shorter intervals

What I got out of it it

A lot of excellent examples of scale and new examples of how it permeates our everyday lives

A resilient structure or system is one which can bounce back to its original form after some stimulus. This book describes how to make more resilient systems and businesses in order to better deal with our increasingly volatile world. Resilience is a common characteristic of dynamic systems which persist over time which is why most organisms embody characteristics of resilience to varying degrees

Key Takeaways

Volatility is increasing and here to stay. The details are different but they share certain common characteristics and are always the result of many complex interactions. Can’t control this type of disruption but we can build better systems by making them more resilient, having the ability to rebound and adapt. Continuity and recovery in the face of change

To improve your resilience is to increase the effort it takes for a stimulus to force you off your baseline while also increasing your ability to adapt and bounce back once it happens. Preserving adaptive capacity. Truly resilient systems change dynamically to achieve its purpose as well as the scale at which it operates. Diversifying the resources in which the system operates makes it more resilient to change as it allows for modularity. Diverse at their edges but simple at their core – modularity, simplicity and interoperability vital

The ways to adapt and the stimuli which force change are both nearly infinite

Resilience is not robustness – robustness typically entails hardening the assets of a business. Redundancy is keeping a backup but is not resiliency either. Resilience is also not the recovery of a system to its initial state.

Think of a tree which is strong but has no give. It can withstand a lot until it snaps. This is robust but not resilient

Now, imagine bamboo. It is thin, flexible and can return to its original state given pretty much any wind. This is resilience

Failures are often helpful to release resources and reset and trying to stop these small failures make systems more fragile and will eventually lead to a massive failure. A seemingly perfect system is often the most fragile and the one which fails often but in small ways may be the most resilient

Psychic resilience comes from habits of mind and is able to be learned and improved upon over time.

Optimism and confidence are some of the best traits to deal with depression and to become more resilient

People exhibiting ego-resilience and ego-control are best at delaying gratification, being resilient and overcoming obstacles

Hardiness – believe can find a meaningful purpose in life, one can influence one’s surrounding and events, both positive and negative events will have lessons one can learn from. People of faith tend to be more resilient partially due to their “hardiness”

Mindfulness meditation is a great tool to improve our resilience as it helps us create a space between our events, thoughts, emotions – an external “witness observer”

Strong social resilience is found in societies with a lot of trust, a translational leader at it’s core promoting adaptive governance

Holism – bolstering the resilience of only one part of the system sometimes adds fragility to another area. To improve resilience you often need to work in more than one mode and one scale and one silo at a time. Take the granular and the global into account simultaneously

Robust yet fragile – systems which are resilient to anticipated danger or change but not to the unanticipated. It is often thousands of small decisions which aggregate rather than one massive event which brings down a system

Must be able to measure health of a system as a whole and not just its pieces to know if fragility is sneaking in

In risk management, risks tend to be modeled as additive but in reality they are multiplicative. One failure makes future failures multiples more likely

Signs of a system flip – becomes unstable near its threshold, too much synchrony or agents acting in union (over correlation and people must make similar choices to survive)

The timing of force, change and its effects is often more important than its scope

Real time data, better monitoring and isolation upon any sign of cascading failure are three important design features

Protocols are the lingua Franca of systems

There are universal scaling laws for biological organisms so that the larger the organism the slower the metabolism and the longer the average life span. The power of clustering comes from a similar phenomenon but in the case of cities, the larger they get, the “faster” they become and the average income increases but certain quality of life markers decrease – there are increasing returns to scale, super linear scaling. However, as this part of life increases, the pace of innovation needs to speed up too or else the city may spiral downwards. The increasing diversity helps with this

Respect is the cheapest concession you can give in relationships and negotiation. It is also a positive sum trait where your dispersal of respect only increases the total

Improving resilience is not about removing every possible disturbance. In fact, facing challenges which test you or your organization are vital. They show where improvements need to be made and can clear the path for creative destruction

What I got out of it

A thorough overview of what resilience entails and many examples of both fragile and antifragile people, ecosystems, institutions, organizations and more

Natural selection is important, but it has not labored alone to craft the fine architectures of the biosphere. Self-organization is the root source of order and is not merely tinkered, but arises naturally and spontaneously because of the principles of self-organization. Self-organization works together with natural selection to help shape and drive evolution in species

Key Takeaways

Science has taken away our paradise – purpose and values are ours alone to make – job today is to reinvent the sacred and Kauffman believes that complexity may contain the answer

Complexity suggests that not all order is accidental and is responsible for much of the spontaneous order seen throughout the world

May lie at the heart of the origin of life and leads to order found in organisms today

Life, therefore, is to be expected and is not an accident if it arises out of fundamental self-organizing principles

Spontaneous order and natural selection have always worked together

Second law of thermodynamics – order tends to disappear in equilibrium systems

Best models explain and predict but failure to predict does not equal failure to understand or explain, especially with chaotic systems. Can find deep theories without knowing every detail (don’t have to know every detail of ontogeny (development of an adult organism) but we can understand it – spontaneous order which then natural selection goes on to mold)

For most systems, equilibrium = death

Order for free – order arises spontaneously and naturally and leads to self-organized systems and emergent properties

Life would then be able to emerge full-grown from a primordial soup and would not need to be built one component at a time – life emerges whole and not piece meal

Life is a natural property of complex chemical systems and that when the number of different kinds of molecules in a chemical soup pass a certain threshold, a self-sustaining network of reactions – an autocatalytic metabolism – will suddenly appear

Life did come from non-life – reduces biology to physics and chemistry

Must pass the subcritical / supracritical threshold

Life exists in between order and chaos – in a kind of phase transition where it is best able to coordinate complex activities and evolve

The very nature of coevolution is to attain this edge of chaos, a self-organized criticality, a web of compromises where each species prospers as well as possible but where none can be sure if its best next step will set off a trickle or a landslide

This world does not lend itself to long-term prediction, we cannot know the true consequences of our own best actions. All we players can do is be locally wise, not globally wise

All living things seem to have a minimal complexity, below which it is impossible to go

Matter must reach a threshold of complexity in order to spring to life – this is inherent to the very nature of life

Living organisms are autocatalytic systems – living organisms began as a system of chemicals that had the capacity to catalyze its own self-maintaining and self-reproducing metabolism once a sufficiently diverse mix of molecules accumulates. Once this threshold is reached, a vast web of catalyzed reactions will crystallize. Such a web, it turns out, is almost certainly autocatalytic – almost certainly self-sustaining, alive. Life emerges as a phase transition once the subcritical threshold of reactions to chemicals is breached

The spontaneous emergence of self-sustaining webs is so natural and robust that it is even deeper than the specific chemistry that happens to exist on earth; it is rooted in mathematics itself

There is an inevitable relationship among spontaneous order, robustness, redundancy, gradualism, and correlated landscapes. Systems with redundancy have the property that many mutations cause no or only slight modifications in behavior. Redundancy yields gradualism. But another name for redundancy is robustness. Robust properties are ones that are insensitive to many detailed alterations. Robustness is precisely what allows such systems to be molded by gradual accumulation of variations – the stable structures and behaviors are ones that can be molded

Homeostasis, the ability to survive small perturbations, required for life to survive

Complexity – orderly enough to ensure stability but flexible enough to adapt and exhibit surprises – evolution takes life to the edge of chaos

Organisms evolve to the subcritical-supracritical boundary which exhibit a power law distribution of events

Be smart by being dumb – have a huge sample set and choose what serves your purpose (don’t be ideological, go with promising evidence over beautiful theory)

Immune system is a universal tool box – ability to produce 100m + antibodies allows you to recognize and respond to any threat

Cambrian pattern of evolution – It is a general principle that innovations are followed by rapid, dramatic improvements in a variety of very different directions followed by successive improvements that are less and less dramatic.

Learning curve – After each improvement, the number of directions for further improvement falls by a constant fraction – an exponential slowing of improvement (applies to technology, evolution, business, mastering skills, any improvement!)

The more complex the system, the more difficult it is to make and accumulate useful drastic changes through natural selection

Correlation length – taking massive jumps can lead to fitter mutations if land at a fitter peak – explore and try vastly different areas to possibly get outsized rewards (deep fluency in many fields and iterate constantly with small bets and pursue promising areas – parallel traced scan)

When fitness is average, the fittest variants will be found far away but as fitness improves, the fittest variants will be found closer and closer to the current position. Expect to find dramatically different variants emerging during early stages of an adaptive process but later the fitter variants that emerge should be ever less different

When fitness is low, there are may directions uphill. As fitness improves, the number of directions uphill dwindles. Thus we expect the branching process to be bushy initially, branching widely at its base, and then branching less and less profusely as fitness increases

Optimal solutions to one part of the overall design problem conflict with optimal solutions to other parts of the overall design. Then we must find compromise solutions to the joint problem that meet the conflicting restraints of the different subproblems

Take a hard, conflict-laden task in which many parts interact and divide it into a quilt of nonoverlapping patches. Try to optimize within each patch. As this occurs, the couplings between part in two patches across patch boundaries will mean that finding a “good” solution in on patch will change the problem to be solved by the parts in adjacent patches… – models coevolving ecosystems

If a problem is complex and full of conflicting constraints, break it into patches and let each patch try to optimize such that all patches coevolve with one another

May not give us the solution to the real problem but may teach us how to learn about the real problem, how to break it into quilt patches that coevolve to find excellent solutions

Ignoring certain subsets of restraints may be helpful at times – should not please all of the people all of the time but you should pay attention to everyone some of the time

What I got out of it

Spontaneous self-organization is a deep, fundamental principles of math, physics, life. Order for free, patch procedure, learning curves and the Cambrian diversity principle, subcritical and supracritical threshold breach is the same thing as phase transition, all we can do is be locally wise and not globally wise since our system is too complex to predict

I spent a couple months reading deliberately on complexity and its many off-shoots and applications. After summarizing the books I read and wrangling with the concepts for some time, I have attempted to make a distilled “teacher’s reference guide” or cheat sheet which (hopefully) describes the key terms and ideas in a clear, concise and applicable manner.

*The vast majority of the content is from the books and not my own words. I’ve simply distilled, compiled, and added a few notes. This is clearly my amateur attempt which I’m sure has many points that experts would refute or disapprove of. Please reach out with any suggestions as I plan to iterate and improve this document over time.

Holland walks us through how coherence emerges from unstructured agents in environments of continuous flux; coherence under change and complex adaptive systems (CAS)

Key Takeaways

Behavior depends much more upon interactions of agents than their actions

Catalog of all activities does not equal understanding the effect of changes in the ecosystem

Many complex systems show coherence in face of change through extensive interactions, aggregation of diverse elements and learning/adaptation

Must understand the interactions and dynamics of the system before can hope to make any significant, lasting changes

Theory can help detect lever points where small changes lead to big outcomes – Amplifier Effect

Cross-disciplinary comparisons are vital as subtle characteristics in one context can be easily drawn out in others

CAS systems made up of a large number of active elements diverse in form and capability

Makes system stronger and more robust. Weeding out weak actors so that only the strong remain counter-intuitively leads to worse performance

Rules are used to describe agent’s strategies – few, simple rules can lead to complex behavior

A major part of the modeling effort for any CAS, then, goes into selecting and representing stimuli and responses, because the behaviors and strategies of the component agents are determined thereby. Once we specify the range of possible stimuli and the set of allowed responses for a given agent, we have determined the kinds of rules that agent can have

Adaptation – process by which an organism best fits itself to its environment

Time scale of adaptation varies drastically and they are very important to take into account in any system (humans vs. trees)

The fast dynamics will shape the slow

Overall, we will view CAS as systems composed of interacting agents described in terms of rules. These agents adapt by changing their rules as experience accumulates. In CAS, a major part of the environment of any given adaptive agent consists of other adaptive agents, so that a portion of any agent’s efforts at adaptation is spent adapting to other adaptive agents, co-evolution (Red Queen). This one feature is a major source of the complex temporal patterns that CAS generate

The 7 Basics

Aggregation

Simplifies complex systems by grouping similar things which leads to constructing models as these are prime building blocks

Facilitates the formation of aggregates as tags manipulate symmetries (flag as a rallying cry which helps group people together)

Tags enable us to observe and act on properties previously unobservable due to symmetries (spinning white cue ball harder to spot but when a stripe is added you can easily tell in which direction it is rotating)

Tags almost always define the network by delimiting the critical interactions, the major connections. Tags acquire acquire this role because the adaptive processes that modify CAS select for tags that mediate useful interactions and against tags that cause malfunctions

Multiplier Effect – resource injected in one node spreads over network which leads to chain of changes (big in network/flows modeling)

Recycling Effect – the effect of cycles in the network can drastically increase output of the system over time as the system retains resources and these resources can be further exploited as they offer new niches to be exploited by new kinds of agents. This process leads to increasing diversity through increasing recycling (virtuous cycle)

Diversity

Each agent fills a niche which is determined based on interactions centering on that agent

Nature abhors vacuums and will fill empty niche with new agent – typically similar in form and habit (the convergence effect, mimicry)

Mechanisms CAS used to anticipate – eliminate details so that selected patterns are emphasized. Agent must select patterns in the torrent of input it receives and then must convert those patterns into change sin its internal structure

A model allows us to infer something about the thing modeled

Tacit and overt models

Tacit simply prescribes a current action, under an implicit prediction of some desired future state

Overt model is used as a basis for explicit, but internal, explorations of alternations, a process often called lookahead

Natural selection selects for better internal models

Building blocks

Deconstruct complex problem into simpler parts which can be used and reused in different circumstances

The search for powerful building blocks is the most effective way to make the best internal models

We can a significant advantage when we can reduce the building blocks at one level to interactions and combinations of building blocks at a lower level: the laws at the higher level derive from the laws at the lower level building blocks. This does not mean that the higher level laws are easy to discover but it does add a tremendous interlocking strength to understanding systems and hierarchies

CAS exhibit coherence under change via conditional action and anticipation and do so with no central controller.

Can discover lever points if can uncover general principles which govern CAS dynamics

Agents must act somewhat similarly if a uniform approach to CAS is feasible

Adaption – a rule’s ability to win based on its usefulness int he past – older rules are likely best as they’ve been tested by time

Credit Assignment to best rules easiest when have immediate feedback – tests the rule’s utility

Bucket Brigade – the credit assignment procedure which strengthens rules that belong to chains of action terminating in rewards

Agent should prefer rules which use more information about a situation

Higher specificity leads to stronger rules (higher in the hierarchy)

Default hierarchy – early on, agents will depend on overly general default rules that serve better than random actions. As experience accumulates, these internal models will be modified by adding competing, more specific exception rules. These will interact symbiotically with the default rules and the resulting model is called a default hierarchy. Default hierarchies expand over time from general default to specific exceptions (the young man knows the rules, the old man the exceptions)

Adaptation by rule discovery – trial and error may work but doesn’t leverage system experience

Plausibility – take strong rules and apply to new areas which seem promising

Recombination of rules leads to discovery and occasionally mutation which can produce a more fit offspring

More fit building blocks are used more frequently which are then passed on more often to succeeding generations

More complicated building blocks usually formed by combining simpler blocks

The higher level are typically composed of well-tested, above-average simpler blocks. Over time, the hierarchy becomes more elaborate, providing for the persistence of still more complex behavior. A

Reproduction, recombination and replacement (genetic algorithm) found in nearly every CAS system

Implicit parallelism – individuals (no matter how great) don’t recur but their building blocks do

Evolution “remembers” combinations of building blocks which increase fitness

Discovery of new building blocks leads to a slew of new innovations (punctuated equilibrium)

With any model, must know what has been emphasized (exaggerated) to make a point and what has been left out to keep focused on that point

Hierarchy – the appearance of new levels of an organization in this evolution depends on one critical ability: each new level must collect and protect resources in a way that outweighs the increased cost of a more complex structure. If the seeded aggregate collects resources rapidly enough to “pay” for the structured complexity, the seed will spread.

Successful approach to any theory – interdisciplinary; computer-based thought experiments, a correspondence principle (models should encompass standard models from prior studies in relevant disciplines); a mathematics of competitive processes based on recombination

What I got out of it

Fascinating book on how the universe seems to produce order for free via coherence, spontaneous self-organization and complex adaptive systems.

A primer on problem solving on scales from local to global, how systems exist and react in the real world while acknowledging that all models are false although they help us simplify and at times make better predictions